Our investigation into generalizing from easy to hard examples builds on several bodies of work. The basic neural architectures we use are not new and build upon prior studies of weight sharing and recurrence [1]}, [2]}, [3]}, [4]}, [5]}, [6]}, [7]}. Networks with variable numbers of test time iterations/layers have also been studied, including variable depth networks [8]}, [9]}, [10]}, [11]}. Motivated by the fact that input sequence length is not necessarily correlated with the computational burden required to solve a problem, [8]} develops a method for RNNs to adaptively select a compute time limit. This work considers only sequence inputs and shows the benefits of decoupling compute budget from input length. A differentiable extension of the technique can also be applied to visual question answering [11]}.
